{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 对数几率回归 pytorch版本\n",
    "## 函数\n",
    "$$\n",
    "z=w^{T}x+b\n",
    "$$\n",
    "$$\n",
    "y=\\frac{1}{1-e^{-z}}\n",
    "$$"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import torch"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 数据\n",
    "\n",
    "编号,色泽,根蒂,敲声,纹理,脐部,触感,密度,含糖率,好瓜 </br>\n",
    "1,青绿,蜷缩,浊响,清晰,凹陷,硬滑,0.697,0.46,是</br>\n",
    "2,乌黑,蜷缩,沉闷,清晰,凹陷,硬滑,0.774,0.376,是</br>\n",
    "3,乌黑,蜷缩,浊响,清晰,凹陷,硬滑,0.634,0.264,是</br>\n",
    "4,青绿,蜷缩,沉闷,清晰,凹陷,硬滑,0.608,0.318,是</br>\n",
    "5,浅白,蜷缩,浊响,清晰,凹陷,硬滑,0.556,0.215,是</br>\n",
    "6,青绿,稍蜷,浊响,清晰,稍凹,软粘,0.403,0.237,是</br>\n",
    "7,乌黑,稍蜷,浊响,稍糊,稍凹,软粘,0.481,0.149,是</br>\n",
    "8,乌黑,稍蜷,浊响,清晰,稍凹,硬滑,0.437,0.211,是</br>\n",
    "9,乌黑,稍蜷,沉闷,稍糊,稍凹,硬滑,0.666,0.091,否</br>\n",
    "10,青绿,硬挺,清脆,清晰,平坦,软粘,0.243,0.267,否</br>\n",
    "11,浅白,硬挺,清脆,模糊,平坦,硬滑,0.245,0.057,否</br>\n",
    "12,浅白,蜷缩,浊响,模糊,平坦,软粘,0.343,0.099,否</br>\n",
    "13,青绿,稍蜷,浊响,稍糊,凹陷,硬滑,0.639,0.161,否</br>\n",
    "14,浅白,稍蜷,沉闷,稍糊,凹陷,硬滑,0.657,0.198,否</br>\n",
    "15,乌黑,稍蜷,浊响,清晰,稍凹,软粘,0.36,0.37,否</br>\n",
    "16,浅白,蜷缩,浊响,模糊,平坦,硬滑,0.593,0.042,否</br>\n",
    "17,青绿,蜷缩,沉闷,稍糊,稍凹,硬滑,0.719,0.103,否</br>"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "dataset=[]\n",
    "with open('./dataset_西瓜3.txt','rU')  as f :\n",
    "    for line in f.readlines():\n",
    "        line=line.strip('\\n')\n",
    "        lineList=line.split(',')\n",
    "        if lineList[0]=='编号':\n",
    "            continue\n",
    "        else :\n",
    "            X=[ float(lineList[-3]),float(lineList[-2])]\n",
    "            if lineList[-1]=='是':\n",
    "                y=1\n",
    "            else :\n",
    "                y=0\n",
    "            dataset.append([X,y])\n",
    "print(dataset)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[[0.697, 0.46], 1], [[0.774, 0.376], 1], [[0.634, 0.264], 1], [[0.608, 0.318], 1], [[0.556, 0.215], 1], [[0.403, 0.237], 1], [[0.481, 0.149], 1], [[0.437, 0.211], 1], [[0.666, 0.091], 0], [[0.243, 0.267], 0], [[0.245, 0.057], 0], [[0.343, 0.099], 0], [[0.639, 0.161], 0], [[0.657, 0.198], 0], [[0.36, 0.37], 0], [[0.593, 0.042], 0], [[0.719, 0.103], 0]]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/david/anaconda3/envs/torch/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: 'U' mode is deprecated\n",
      "  \n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "w=torch.tensor(np.random.normal(0,0.01,(2,1)),dtype=torch.float32,requires_grad=True)\n",
    "b=torch.zeros(1,dtype=torch.float32,requires_grad=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "def model(X,w,b):\n",
    "    y=torch.mm(X,w)+b\n",
    "    return 1/(1+torch.exp(-y))\n",
    "def log_loss(y_hat, y):\n",
    "    return -(y*(torch.log(y_hat) + (1 - y) * torch.log(1 - y_hat)))\n",
    "def sgd(params,lr):\n",
    "    for param in params:\n",
    "        param.data-=lr*param.grad\n",
    "lr=0.05\n",
    "for epoch in range(100):\n",
    "    l=0\n",
    "    X=[]\n",
    "    y=[]\n",
    "    for i in range(len(dataset)):\n",
    "        X_=dataset[i][0].copy()\n",
    "        X.append(X_)\n",
    "        y_=dataset[i][1]\n",
    "        y.append(y_)\n",
    "    X=torch.tensor(X,dtype=torch.float32)    \n",
    "    l=log_loss(model(X,w,b),torch.tensor(y)).sum()\n",
    "    l.backward()\n",
    "    sgd([w,b],lr)\n",
    "    w.grad.data.zero_()\n",
    "    b.grad.data.zero_()\n",
    "    print('epoch: %d, loss: %f' % (epoch + 1, l/17))\n",
    "\n",
    "\n",
    "\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "epoch: 1, loss: 0.045436\n",
      "epoch: 2, loss: 0.043288\n",
      "epoch: 3, loss: 0.041337\n",
      "epoch: 4, loss: 0.039555\n",
      "epoch: 5, loss: 0.037923\n",
      "epoch: 6, loss: 0.036422\n",
      "epoch: 7, loss: 0.035036\n",
      "epoch: 8, loss: 0.033753\n",
      "epoch: 9, loss: 0.032562\n",
      "epoch: 10, loss: 0.031453\n",
      "epoch: 11, loss: 0.030418\n",
      "epoch: 12, loss: 0.029449\n",
      "epoch: 13, loss: 0.028540\n",
      "epoch: 14, loss: 0.027687\n",
      "epoch: 15, loss: 0.026884\n",
      "epoch: 16, loss: 0.026126\n",
      "epoch: 17, loss: 0.025410\n",
      "epoch: 18, loss: 0.024733\n",
      "epoch: 19, loss: 0.024092\n",
      "epoch: 20, loss: 0.023483\n",
      "epoch: 21, loss: 0.022904\n",
      "epoch: 22, loss: 0.022353\n",
      "epoch: 23, loss: 0.021829\n",
      "epoch: 24, loss: 0.021329\n",
      "epoch: 25, loss: 0.020851\n",
      "epoch: 26, loss: 0.020394\n",
      "epoch: 27, loss: 0.019958\n",
      "epoch: 28, loss: 0.019539\n",
      "epoch: 29, loss: 0.019138\n",
      "epoch: 30, loss: 0.018753\n",
      "epoch: 31, loss: 0.018384\n",
      "epoch: 32, loss: 0.018029\n",
      "epoch: 33, loss: 0.017687\n",
      "epoch: 34, loss: 0.017359\n",
      "epoch: 35, loss: 0.017042\n",
      "epoch: 36, loss: 0.016737\n",
      "epoch: 37, loss: 0.016442\n",
      "epoch: 38, loss: 0.016158\n",
      "epoch: 39, loss: 0.015884\n",
      "epoch: 40, loss: 0.015619\n",
      "epoch: 41, loss: 0.015362\n",
      "epoch: 42, loss: 0.015114\n",
      "epoch: 43, loss: 0.014874\n",
      "epoch: 44, loss: 0.014642\n",
      "epoch: 45, loss: 0.014416\n",
      "epoch: 46, loss: 0.014198\n",
      "epoch: 47, loss: 0.013986\n",
      "epoch: 48, loss: 0.013780\n",
      "epoch: 49, loss: 0.013581\n",
      "epoch: 50, loss: 0.013387\n",
      "epoch: 51, loss: 0.013198\n",
      "epoch: 52, loss: 0.013015\n",
      "epoch: 53, loss: 0.012837\n",
      "epoch: 54, loss: 0.012664\n",
      "epoch: 55, loss: 0.012495\n",
      "epoch: 56, loss: 0.012331\n",
      "epoch: 57, loss: 0.012171\n",
      "epoch: 58, loss: 0.012016\n",
      "epoch: 59, loss: 0.011864\n",
      "epoch: 60, loss: 0.011716\n",
      "epoch: 61, loss: 0.011572\n",
      "epoch: 62, loss: 0.011431\n",
      "epoch: 63, loss: 0.011293\n",
      "epoch: 64, loss: 0.011160\n",
      "epoch: 65, loss: 0.011029\n",
      "epoch: 66, loss: 0.010901\n",
      "epoch: 67, loss: 0.010776\n",
      "epoch: 68, loss: 0.010654\n",
      "epoch: 69, loss: 0.010534\n",
      "epoch: 70, loss: 0.010418\n",
      "epoch: 71, loss: 0.010304\n",
      "epoch: 72, loss: 0.010192\n",
      "epoch: 73, loss: 0.010083\n",
      "epoch: 74, loss: 0.009976\n",
      "epoch: 75, loss: 0.009872\n",
      "epoch: 76, loss: 0.009769\n",
      "epoch: 77, loss: 0.009669\n",
      "epoch: 78, loss: 0.009571\n",
      "epoch: 79, loss: 0.009474\n",
      "epoch: 80, loss: 0.009380\n",
      "epoch: 81, loss: 0.009288\n",
      "epoch: 82, loss: 0.009197\n",
      "epoch: 83, loss: 0.009108\n",
      "epoch: 84, loss: 0.009021\n",
      "epoch: 85, loss: 0.008935\n",
      "epoch: 86, loss: 0.008852\n",
      "epoch: 87, loss: 0.008769\n",
      "epoch: 88, loss: 0.008689\n",
      "epoch: 89, loss: 0.008609\n",
      "epoch: 90, loss: 0.008531\n",
      "epoch: 91, loss: 0.008455\n",
      "epoch: 92, loss: 0.008380\n",
      "epoch: 93, loss: 0.008306\n",
      "epoch: 94, loss: 0.008234\n",
      "epoch: 95, loss: 0.008162\n",
      "epoch: 96, loss: 0.008092\n",
      "epoch: 97, loss: 0.008024\n",
      "epoch: 98, loss: 0.007956\n",
      "epoch: 99, loss: 0.007889\n",
      "epoch: 100, loss: 0.007824\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "print(w,b)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[2.7305],\n",
      "        [1.1025]], requires_grad=True) tensor([5.3684], requires_grad=True)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "def pred(w,b,dataset):\n",
    "    with torch.no_grad():\n",
    "        y_hat=[dataset[i][1] for i in range(0,len(dataset)) ]\n",
    "        result=[]\n",
    "        for i in range(0,len(dataset)):\n",
    "            X_hat=dataset[i][0].copy()\n",
    "            X_hat=torch.tensor(X_hat)\n",
    "            # print(X_hat)\n",
    "            # print(w)\n",
    "            # print(b)\n",
    "            y_pred=X_hat[0]*w[0]+X_hat[1]*w[1]+b\n",
    "            y_pred=1/(1+torch.exp(-y_pred))\n",
    "            print(y_pred)\n",
    "            y=dataset[i][1]    \n",
    "            result.append(y_pred.numpy())\n",
    "\n",
    "    return result,y_hat\n",
    "# print(result)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "\n",
    "result,y_hat=pred(w,b,dataset)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([0.9996])\n",
      "tensor([0.9996])\n",
      "tensor([0.9994])\n",
      "tensor([0.9994])\n",
      "tensor([0.9992])\n",
      "tensor([0.9988])\n",
      "tensor([0.9989])\n",
      "tensor([0.9989])\n",
      "tensor([0.9993])\n",
      "tensor([0.9982])\n",
      "tensor([0.9978])\n",
      "tensor([0.9984])\n",
      "tensor([0.9993])\n",
      "tensor([0.9994])\n",
      "tensor([0.9988])\n",
      "tensor([0.9991])\n",
      "tensor([0.9994])\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "\n",
    "x=[i for i in range(0,len(result))]\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(x,result)\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(x,y_hat)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.6.8",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.8 64-bit ('torch': conda)"
  },
  "interpreter": {
   "hash": "2b23fc4612d2a20491382ea15c73e42619afc437d67ed04c4074dd835ed863ea"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}